Finding a Solution, all Solutions, or the Most Probable Solution to a Temporal Interval Algebra Network
Over the years, many implementations have been
proposed for solving IA networks. These implementations are
concerned with finding a solution efficiently. The primary goal of
our implementation is simplicity and ease of use.
We present an IA network implementation based on finite domain
non-binary CSPs, and constraint logic programming. The
implementation has a GUI which permits the drawing of arbitrary IA
networks. We then show how the implementation can be extended to
find all the solutions to an IA network. One application of finding all
the solutions, is solving probabilistic IA networks.
[1] J.F. Allen. Towards a general model of action and time, Artificial
Intelligence, 23(2), 1984, p. 123-154.
[2] T. Fruhwirth. Temporal reasoning with constraint handling rules,
Technical report ECRC-94-05, European computer-industry research
centre, Germany, 1994.
[3] E. Lamma, M. Milano, and P. Mello. Temporal reasoning in a meta
constraint logic programming architecture, Third international workshop
on temporal representation and reasoning (TIME-96), Florida, 1996, p.
128-135.
[4] J. Thornton, M. Beaumont, A. Sattar, and M. Maher. A local search
approach to modeling and solving interval algebra problems, The journal
of logic and computation, 4(1), 2004, p. 93-112.
[5] E. Tsang. Foundations of constraint satisfaction, Academic Press, 1993.
[6] P. van Beek and D.W. Manchak. The design and experimental analysis
of algorithms for temporal reasoning, Journal of Artificial Intelligence
Research, 4, 1996, p. 1-18.
[1] J.F. Allen. Towards a general model of action and time, Artificial
Intelligence, 23(2), 1984, p. 123-154.
[2] T. Fruhwirth. Temporal reasoning with constraint handling rules,
Technical report ECRC-94-05, European computer-industry research
centre, Germany, 1994.
[3] E. Lamma, M. Milano, and P. Mello. Temporal reasoning in a meta
constraint logic programming architecture, Third international workshop
on temporal representation and reasoning (TIME-96), Florida, 1996, p.
128-135.
[4] J. Thornton, M. Beaumont, A. Sattar, and M. Maher. A local search
approach to modeling and solving interval algebra problems, The journal
of logic and computation, 4(1), 2004, p. 93-112.
[5] E. Tsang. Foundations of constraint satisfaction, Academic Press, 1993.
[6] P. van Beek and D.W. Manchak. The design and experimental analysis
of algorithms for temporal reasoning, Journal of Artificial Intelligence
Research, 4, 1996, p. 1-18.